Comparison of Multi-Label Classification Algorithms for Code Smell Detection


Kiyak E. O., Birant D., Birant K. U.

3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019, Ankara, Turkey, 11 - 13 October 2019 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/ismsit.2019.8932855
  • City: Ankara
  • Country: Turkey
  • Keywords: code smell detection, machine learning, multi-label classification, software engineering
  • Dokuz Eylül University Affiliated: Yes

Abstract

© 2019 IEEE.Code smells in a source code shows the weakness of design or implementation. To detect code smells, several detection tools have been developed. However, these tools generally produce different results, since code smells are subjectively interpreted, informally defined and configured by the developers, domain-dependent and based on opinions and experiences. To cope with these issues, in this paper, we have used machine learning techniques, especially multi-label classification methods, to classify whether the given source code is affected with more than one code smells or not. We have conducted experiments on four code smell datasets and transformed them into two multi-label datasets (one for method level and the other one for class level). Two multi-label classification methods (Classifier Chains and Label Combination) and their ensemble models performed on the converted datasets using five different base classifiers. The results show that, as a base classifier, Random Forest algorithm performs better than Decision Tree, Naive Bayes, Support Vector Machine and Neural Network algorithms.